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NVIDIA NCP-ADS Exam Syllabus Topics:
| Section | Weight | Objectives |
|---|---|---|
| Data Manipulation and Software Literacy | 19% | - Data processing libraries selection and usage - Dependency management and containerization - GPU-accelerated ETL workflows - Performance profiling and optimization tools |
| MLOps | 19% | - Pipeline automation and orchestration - End-to-end workflow management - Monitoring, logging and maintenance - Model deployment and serving |
| GPU and Cloud Computing | 16% | - GPU architecture and acceleration principles - Cloud GPU environments and deployment - CRISP-DM and data science methodology - Resource management and scaling strategies |
| Data Analysis | 14% | - Exploratory Data Analysis (EDA) - Time-series analysis and anomaly detection - Distributed and parallel data processing - Data visualization and graph analytics |
| Data Preparation | 17% | - Data cleaning, preprocessing and transformation - Data validation and quality assurance - Feature engineering and data type optimization - Workflow monitoring and bottleneck identification |
| Machine Learning | 15% | - GPU-accelerated ML frameworks and algorithms - Model training and hyperparameter tuning - Distributed training strategies - Model evaluation and validation |
NVIDIA-Certified-Professional Accelerated Data Science Sample Questions:
You are working on a machine learning pipeline using NVIDIA RAPIDS cuML and need to standardize the dataset to ensure that all features have a mean of 0 and a standard deviation of 1.
Which of the following methods should you use to achieve this in cuML?
- A. cuml.preprocessing.StandardScaler()
- B. cuml.preprocessing.MinMaxScaler()
- C. cuml.preprocessing.LabelEncoder()
- D. cuml.preprocessing.OneHotEncoder()
Correct Answer: A 🗳️
A data scientist is training a deep learning model and wants to find the best learning rate to optimize convergence speed and generalization. The scientist tests different values: A very small learning rate (0.00001) results in slow convergence.
A very large learning rate (10) causes the model loss to fluctuate wildly and not converge.
Which of the following strategies is the most effective way to optimize the learning rate dynamically during training?
- A. Decrease the learning rate to zero at the end of training (learning rate scheduling)
- B. Use learning rate warm-up followed by decay
- C. Use a fixed learning rate chosen through trial and error
- D. Use the same learning rate for all layers in a deep neural network
Correct Answer: B 🗳️
You are working with a large dataset that contains missing values in multiple columns. Your goal is to prepare this dataset for training a machine learning model on an NVIDIA GPU using RAPIDS.
Which of the following approaches is the most efficient method to handle missing values in this scenario?
- A. Drop all rows containing missing values using Pandas before transferring data to the GPU
- B. Convert the dataset to a NumPy array and manually replace missing values with the mean
- C. Use fillna() with a fixed value on the GPU using cuDF
- D. Apply a deep learning-based imputation model before moving data to the GPU
Correct Answer: C 🗳️
You need to set up an isolated, GPU-accelerated environment for a deep learning project that requires specific CUDA, cuDNN, and RAPIDS versions.
Which of the following best ensures a reproducible environment using Docker?
- A. Use a system-wide CUDA installation and mount the /usr/local/cuda directory into the container to provide GPU support.
- B. Use the nvidia/cuda base image and specify the required RAPIDS and deep learning libraries in a Dockerfile.
- C. Install NVIDIA drivers manually inside a Docker container every time it runs.
- D. Build a container from an Ubuntu base image and manually install all dependencies without specifying versions.
Correct Answer: B 🗳️
You are consulting for a retail company that collects data from daily sales transactions, customer interactions, and inventory tracking across multiple locations. They are unsure whether their dataset qualifies as big data and which processing method would be most suitable.
Which of the following characteristics best indicate that the dataset requires big data processing and acceleration techniques?
- A. The dataset includes a mix of numerical and categorical variables, requiring additional preprocessing
- B. The dataset contains more than 1 million records, making it impossible to process using pandas
- C. The dataset exceeds the memory capacity of a single machine and requires distributed or GPU- accelerated processing
- D. The dataset is stored in multiple relational database tables, making querying inefficient
Correct Answer: C 🗳️
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